{
  "annotation_id": "25f2c646-7556-4b69-a6a7-ad1390b371a4",
  "date_created": "2026-03-02T18:00:53.363000Z",
  "date_modified": "2026-03-02T18:00:53.363000Z",
  "file_hash": "0f91737d29460c8e3bf5471a92dd3e47cfc2c81757c048100796fa87f6b2c1fa",
  "private": false,
  "record": {
    "abstract": "In this work we present a new idea to develop a method to separate stochastic\nand deterministic information contained in an electrocardiogram, ECG, which may\nprovide new sources of information with diagnostic purposes. We assume that the\nECG has information corresponding to many different processes related with the\ncardiac activity as well as contamination from different sources related with\nthe measurement procedure and the nature of the observed system itself. The\nmethod starts with the application of an improuved archetypal analysis to\nseparate the mentioned stochastic and deterministic information. From the\nstochastic point of view we analyze Renyi entropies, and with respect to the\ndeterministic perspective we calculate the autocorrelation function and the\ncorresponding correlation time. We show that healthy and pathologic information\nmay be stochastic and/or deterministic, can be identified by different measures\nand located in different parts of the ECG.",
    "arxiv_id": "physics/0408134",
    "authors": [
      "R. M. Guti\u00e9rrez",
      "L. Sandoval"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1238/Physica.Topical.118a00132",
    "title": "A Method to Separate Stochastic and Deterministic Information from   Electrocardiograms",
    "url": "https://arxiv.org/abs/physics/0408134"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "4fa43446-f8ca-4038-b172-ab2a402bb5dc",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}